Summary of Work: Mismeasured or missing data can lead to false or misleading conclusions and present a widespread problem in a variety of biomedical fields, including environmental epidemiology. This project seeks to develop new statistical methods and to apply existing methods to address challenges to valid statistical estimation and testing posed by mismeasured or missing data. Work continues in three areas: (1) delineating conditions that guarantee that the sign of dose-response trend is preserved in the face of measurement error, (2) methods to impute values for missing data in case-control studies that provide unbiased estimates and correct standard errors yet employ relatively simple techniques, and (3) strategies for intentionally omitting some data in ways that save on study costs while preserving most of the information that would have been available had all data been collected. We have discovered conditions that apply at least to a restricted class of regression models under which the sign of a dose-response trend is preserved when data are mismeasured; we are trying to see how broadly these results may apply. We have seen that imputing the mean response observed among control subjects to missing data for both cases and controls can provide valid inference when an excess relative risk model is used; we are working to adapt these results to the more common logistic model. Early results indicate that when an exposure assessment is costly (e.g., testing serum for toxic metabolites), measuring pooled samples from multiple subjects can cut costs without losing much information.